| Automatic segmentation of MR brain images with a convolutional neural network P Moeskops, MA Viergever, AM Mendrik, LS de Vries, M Benders, I Isgum IEEE Transactions on Medical Imaging 35 (5), 1252-1261, 2016 | 948 | 2016 |
| Deep learning for multi-task medical image segmentation in multiple modalities P Moeskops, JM Wolterink, BHM Van Der Velden, KGA Gilhuijs, T Leiner, ... Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th …, 2016 | 371 | 2016 |
| Benchmark on automatic six-month-old infant brain segmentation algorithms: the iSeg-2017 challenge L Wang, D Nie, G Li, É Puybareau, J Dolz, Q Zhang, F Wang, J Xia, Z Wu, ... IEEE transactions on medical imaging 38 (9), 2219-2230, 2019 | 166 | 2019 |
| Adversarial training and dilated convolutions for brain MRI segmentation P Moeskops, M Veta, MW Lafarge, KAJ Eppenhof, JPW Pluim Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical …, 2017 | 163 | 2017 |
| Structural and resting state functional connectivity of the subthalamic nucleus: identification of motor STN parts and the hyperdirect pathway EJL Brunenberg, P Moeskops, WH Backes, C Pollo, L Cammoun, ... PloS one 7 (6), e39061, 2012 | 141 | 2012 |
| Domain-adversarial neural networks to address the appearance variability of histopathology images MW Lafarge, JPW Pluim, KAJ Eppenhof, P Moeskops, M Veta Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical …, 2017 | 125 | 2017 |
| Deformable image registration using convolutional neural networks KAJ Eppenhof, MW Lafarge, P Moeskops, M Veta, JPW Pluim Medical Imaging 2018: Image Processing 10574, 192-197, 2018 | 114 | 2018 |
| Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in MRI P Moeskops, J de Bresser, HJ Kuijf, AM Mendrik, GJ Biessels, JPW Pluim, ... NeuroImage: Clinical 17, 251-262, 2018 | 108 | 2018 |
| Perioperative neonatal brain injury is associated with worse school‐age neurodevelopment in children with critical congenital heart disease NHP Claessens, SO Algra, TL Ouwehand, NJG Jansen, R Schappin, ... Developmental Medicine & Child Neurology 60 (10), 1052-1058, 2018 | 98 | 2018 |
| Evaluation of automatic neonatal brain segmentation algorithms: the NeoBrainS12 challenge I Išgum, MJNL Benders, B Avants, MJ Cardoso, SJ Counsell, EF Gomez, ... Medical image analysis 20 (1), 135-151, 2015 | 97 | 2015 |
| Automatic segmentation of MR brain images of preterm infants using supervised classification P Moeskops, MJNL Benders, SM Chiţǎ, KJ Kersbergen, F Groenendaal, ... NeuroImage 118, 628-641, 2015 | 86 | 2015 |
| Development of cortical morphology evaluated with longitudinal MR brain images of preterm infants P Moeskops, MJNL Benders, KJ Kersbergen, F Groenendaal, LS de Vries, ... PLOS ONE 10 (7), e0131552, 2015 | 75 | 2015 |
| Relation between clinical risk factors, early cortical changes, and neurodevelopmental outcome in preterm infants KJ Kersbergen, F Leroy, I Išgum, F Groenendaal, LS de Vries, ... Neuroimage 142, 301-310, 2016 | 62 | 2016 |
| Delayed cortical gray matter development in neonates with severe congenital heart disease NHP Claessens, P Moeskops, A Buchmann, B Latal, W Knirsch, I Scheer, ... Pediatric Research 80, 668–674, 2016 | 56 | 2016 |
| Prediction of cognitive and motor outcome of preterm infants based on automatic quantitative descriptors from neonatal MR brain images P Moeskops, I Išgum, K Keunen, NHP Claessens, IC van Haastert, ... Scientific reports 7 (1), 2163, 2017 | 30 | 2017 |
| Severe retinopathy of prematurity is associated with reduced cerebellar and brainstem volumes at term and neurodevelopmental deficits at 2 years FJ Drost, K Keunen, P Moeskops, NHP Claessens, F Van Kalken, I Išgum, ... Pediatric research 83 (4), 818-824, 2018 | 27 | 2018 |
| Changes in brain morphology and microstructure in relation to early brain activity in extremely preterm infants ML Tataranno, NHP Claessens, P Moeskops, MC Toet, KJ Kersbergen, ... Pediatric research 83 (4), 834-842, 2018 | 21 | 2018 |
| Automatic segmentation of the intracranial volume in fetal MR images N Khalili, P Moeskops, NHP Claessens, S Scherpenzeel, E Turk, ... Fetal, Infant and Ophthalmic Medical Image Analysis: International Workshop …, 2017 | 19 | 2017 |
| Automatic extraction of the intracranial volume in fetal and neonatal MR scans using convolutional neural networks N Khalili, E Turk, M Benders, P Moeskops, NHP Claessens, R de Heus, ... NeuroImage: Clinical 24, 102061, 2019 | 18 | 2019 |
| Evaluation of an automatic brain segmentation method developed for neonates on adult MR brain images P Moeskops, MA Viergever, MJNL Benders, I Išgum Medical Imaging 2015: Image Processing 9413, 304-309, 2015 | 18 | 2015 |
Ivana IsgumUniversity Professor of AI and Medical Imaging, Amsterdam University Medical CenterVerified email at amsterdamumc.nl
